![Mg-CO2 Engine
pour Mars
exploration
International conference on low-cost planetary missions [Shafirovitch,
Salomon, Gökalp, 2003]: « Mars rover vs Mars
hopper »](https://image.slidesharecdn.com/cdsphysicswskazakciminesparistech-150116032711-conversion-gate01/75/Innovative-Design-Workshop-HiggsML-and-beyond-Machine-Learning-in-Particle-Physics-24-2048.jpg)
![DKCP process: Linearising C-K dynamics
33!
Akın O. Kazakçı, MINES ParisTech!
Proven
methodology:
-‐
Developped
at
Mines
ParisTech
(TMCI)
with
RATP
and
Thalès
Avionics
-‐ 40+
KCP
by
researchers
(2002-‐2014)
-‐ 2
PhD
Projects
(Arnoux,
2013;
Klasing
Chen,
in
process)
-‐ Now,
a
network
of
specialist
consultants
Ini4alisa4on
[K]
Knowledge
sharing
Workshops
[P]
Project
building
[C]
IFM-‐Design
Workshops
[RUN]](https://image.slidesharecdn.com/cdsphysicswskazakciminesparistech-150116032711-conversion-gate01/75/Innovative-Design-Workshop-HiggsML-and-beyond-Machine-Learning-in-Particle-Physics-33-2048.jpg)
![DKCP process: Linearising C-K dynamics
36!
Management
of
the
cogni4ve
and
social
aspects
(KCP
facilitators)
Innova4on
effort
(Par9cipants;
20-‐50)
D
K
C
P
Pré-‐C
Pré-‐K
Project
organisa9on
Defining
and
pre-‐explora9on
of
K
pockets
Sharing
and
integra9ng
K
Orienta9on
of
phase
C
Guided
crea9vity
Building
ac9onnable
strategies
Akın O. Kazakçı, MINES ParisTech!
Ini4alisa4on
[K]
Knowledge
sharing
Workshops
[P]
Project
building
[C]
IFM-‐Design
Workshops
[RUN]](https://image.slidesharecdn.com/cdsphysicswskazakciminesparistech-150116032711-conversion-gate01/75/Innovative-Design-Workshop-HiggsML-and-beyond-Machine-Learning-in-Particle-Physics-36-2048.jpg)
Uploaded byAkin Osman Kazakci
Innovative Design Workshop - HiggsML and beyond (Machine Learning in Particle Physics)
The document discusses the integration of machine learning applications in particle physics and the need for innovative design methodologies in response to the challenges posed by big data and IoT. It explores design theory, particularly C-K theory, which emphasizes the joint expansion of concepts and knowledge for innovative design, alongside the impact of different design strategies. Additionally, it examines various design examples, challenges in recognizing innovation, and the importance of adaptive strategies in design and data science.
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Innovative Design Workshop - HiggsML and beyond (Machine Learning in Particle Physics)
- 1. Machine Learning Applicationsin Particle Physics - Broadening the scope of exploration Akın O. Kazakçı akin.kazakci@mines-paristech.fr Centre for Data Science January the 12th, 2014
- 2. Design Theory and Methods for Innova4on • Chair for Research and Educa9on • Fundamental Research on Design Theory • 11 Industrial Sponsors • Theory , Field research, History, Laboratory experiments
- 3. Profound Transformation ofNPD activities 3! Akın O. Kazakçı, MINES ParisTech! • New functional spaces • New user experiences • New competencies • New partnerships • New business models • Fuzzy industrial sectors è 3rd Industrial revolution (Le Masson et al., 2006) è New Products vs. New Product Types è Revision of Objects’ Identities (Hatchuel et al., 1999)
- 4. From problem-solving toinnovative design MaHmann, C. A.. « A vision for data science », Nature, 2013. Data deluge imposes a colossal need for breakthrough, è The need to go beyond problem solving Traditional industry (non-data centric; aeronautic, automotive, …) is struggling with big-data and IoT è Problems are not well-formulated A methodological renewal is required è Design theory and methods can help
- 5. Plan 1. What does itmean to define an object?! 2. Design theory: a few basic notions! 3. Examples! 5! Akın O. Kazakçı, MINES ParisTech!
- 6. Design of an innovative“folding chair” An innova9ve object causes some surprise
- 7. How to detect innova9on? « iPhone is 4rth in this ranking, in front of HTC touch dual, but behind Nokia n95 and LG Viewty. » commentcamarche.net, July 2007 In US, during its first week, 500 000 iPhone have been sold. In France 30 000 iPhones sold only in five days following its debut. « Conclusion: Despite some intriguing feature iPhone has, it lacks many vital funcIonaliIes it needs to be a serious threat for replacing Blackberry for business professionnals » Techcrunch.com, Agust 2007 forum de Developpez.com 14/09/09 It might be hard to recognize an innova9on Agogué©.
- 8. Try it! -‐ Red Bull Gravity Challenge You are a designer and you have been asked to produce the most crea9ve solu9on to the following ques9on: Ensure that a hen's egg dropped from a height of 10m does not break.” Agogué©. Being innova9ve: how easy is that? Your turn!
- 9. Experiments with 210 subjets (842 proposiIons) “Fixa4on effects” Three types of solu9ons : Slowing the fall Protec9ng the egg Dumping the schock covers 81 % results! Fixa9ons on an objects iden9ty Surely, you have something be[er?
- 10. 10! Akın O. Kazakçı,MINES ParisTech! Brainstorming is not enough !!!
- 11. Plan 1. What does itmean to define an object?! 2. Design theory: a few basic notions! 3. Examples! 11! Akın O. Kazakçı, MINES ParisTech!
- 12. New products vs.New product categories ? ? ? ? ? A300 A340
- 13. Main functions and design parametersare maintained Rule-‐based design Rule-‐breaking design • New functional spaces • New competencies • New partnerships • New business models Innova4on: op4misa4on or iden4ty change? Innova9on as « op9misa9on » Innova9on as « iden9ty change »
- 14. 14! Akın O. Kazakçı,MINES ParisTech! How to capture revision of identities? – A concept-‐knowledge theory of design « Design specs » Tradi9onal Object Defini9ons: Knowledge Methods, Judgements, R&D Competencies… an example of design specs for locomo9ve engines (1890s’) In design, objects can be defined by a « design spec » -‐ a list of features (or proper9es). The designer (individual or group) need to have some knowledge specific to each « feature » to be able to implement (or build) it and for handling interac9ons.
- 15. Revision of identitiesas « Dual expansive reasoning » ? ? Concept expansions Knowledge expansions In « innova9ve design », both design specs and associated knowledges are « dissolved » and « made to evolve ».
- 16. Source: Wikipedia Hatchuel 96; Hatchuel and Weil 99, 02 Kazakci and Tsoukias, 03; Kazakci 07 16! C-K design theory: a breakthrough in understanding design C-‐K design theory describes innova9ve design as the interac9on and joint expansion of concepts and knowledge. Ø Collec9ve reasoning and ac9on on desired, unknown and undecidable objects Ø Two spaces for exploring: Space of concepts (arborescent explora9on of unfeasible specifica9ons) and knowledge space (proposi4ons about the world – all kinds of knowledge). Ø Opera4ons for iden4ty change : Expansive par44ons (flying ship, free newspaper, mobile museum, camera-‐ glass, … ) A revival of design theory field: Yoshikawa, 81; Suh, 91; Braha and Reich 03; Shai and Reich, 03; Research in Engineering Design, Special Issue on Design Theory (2013), …
- 17. C-‐K theory: design as expansive reasoning (Hatchuel, 96, Hatchuel et Weil 02, 03, …) Concepts (C ) C0 = A flying ship that is not a seaplane Expansive Partition Ø Designing means extending/ building defini9ons of objects by adding/substrac9ng new aHributes/proper9es – un9l a sa9sfactory defini9on emerges Ø In C space, you can expand or restrict a defini9on
- 18. (Hatchuel, 96, Hatchuel et Weil 02, 03, …) Knowledge (K) • « true » propositions about things (or people) Planes, wings, balloons zoology Aerodynamics Outdoor markets Boats and sailing A flying boat that is not a seaplane Proposi4ons are used to: • to formulate and expand concepts : • to test concepts • K-‐dependancy or K-‐illusions Tes4ng concepts generates new knowledge Concepts (C ) C-‐K theory: design as expansive reasoning
- 19. espace C espaceK K0 Existing knowledge base concept initial disjonction K1 Knowledge expansion due to the exploration of new concepts K2 expansion of knowledge due to the previous dual expansions final concept becomes part of K conjonction K à K C à K K à C C à C Quelques rappels
- 20. Exemple d’applica9on A lighter and cheaper camping chair K on camping&chairs C K 1 leg 3 leg 4 leg 0 leg K on equilibria of sirng Equilibria by the object Man+object made equilibria Man-‐made equilibria carpet a swing? hammock? carpets slings hammock A chair that is not a hammock
- 21. Determining expansive pathusing C-K reasoningDetermining fixation path using C-K reasoning Theory-driven experiments – SIG Design Theory 2012 – M.Cassotti & M.Agogué C space K space Expanding both in the C-space and in the K-space
- 22. Result 1 :the paths identified as fixation paths using C-K theory are the ones within the fixation effect for adults Theory-driven experiments – SIG Design Theory 2012 – M.Cassotti & M.Agogué (1) Natural distribution of solutions of a design task
- 23. Do examples belongingto the fixation path and examples belonging to expansive paths have an opposite effect on participants’ ability to generate creative ideas ? Theory-driven experiments – SIG Design Theory 2012 – M.Cassotti & M.Agogué EXPANSIVE EXAMPLE 1 RESTRICTIVE EXAMPLE (2) The impact of examples on creativity EXPANSIVE EXAMPLE 2
- 24. Mg-CO2 Engine pour Mars exploration Internationalconference on low-cost planetary missions [Shafirovitch, Salomon, Gökalp, 2003]: « Mars rover vs Mars hopper »
- 25. C K C0: MgCO2 engine for Mars Missions MgCO2 engine for Mars missions not being sample return Shafirovitch 1996: Mars landed mass is greater with MgCO2 than with classic propellant Consequence: MgCO2 impossible as propulsion system for Mars sample return missions KàK2 Standard knowledge on physics combustion CàK1 Initial phase: interpreting previous studies MgCO2 engine for Mars mission being a sample return missions KàC3 MgCO2 engine for Mars mission being a sample return missions (negative conjunction) Shafirovitch 1996: experiment to evaluate Mg-CO2 specific impulse: OK, enough … with enough specific impulse … without enough specific impulse (negative conjunction)
- 26. Phase 1: avoidingpredefined « functions » C K C0 + !A1 = MgCO2 engine for Mars missions not being sample return Standard knowledge on physics combustion + dK KàC Negative conjonction for C0 + ~A1 + scenario k attributes Scenarios including Mg-CO2 used on Mars perform better than others KàK Mars missions scenarios (logistic model + scientific program) Evaluation (comparison with classical propellant on similar missions on a killer criteria) CàK Scenarios 1, 2…. n KàC
- 27. Phase 2: designingnew « functions » C K Scenarios including Mg-CO2 used on Mars perform better than others Planned + A4: Unplanned Mobility on Mars: range, speed, terrain… sensitivity to environment conditions or opportunities. Planned vs unplanned C0 + ~A1 = MgCO2 engine for Mars missions not being sample return Standard knowledge on physics combustion + dK Somewhere else… + A2: Mg- CO2 only used on Mars + A3: Mobility Science Potential uses of an engine on Mars: science, mobility, communication…Other uses Comm.
- 28. Winning strategy Achieve 5σ! Select a classifica9on method! Pre-‐processing! Choose hyper-‐params! Train! Op9mize for accuracy! SVM Decision Trees NN …..….. Integrate AMS directly in training during Gradient Boos9ng (John) Dicovery condi4on: A discovery is claimed when we … Problem formula4on: Tradi9onal classifica9on serng… Cross-‐Valida4on: Techniques for evalua9ng how a … Ensemble Methods during node split in random forest (John) Weighted Classifica9on Cascades ? ? ? ? ? Op4miza4on of AMS Design for sta9s9cal efficiency The biggest challenge is the unstability of AMS. Compe44on results clearly show that only par4cipants who dealt effec4vely with this issue have had higher ranks. 1st 2nd 3rd Ensembles + CV monitoring + cutoff threshold seem to be a winning strategy monitoring progress with CV + ensembles + selec9ng a cutoff threshold that op9mise (or stabilise AMS) Public guide to AMS 3.6 « moves » many par9cipants to the given path Fixa9on vs. Crea9ve Authority (Agogué et al, 2014)
- 29. monitoring progress with CrossValida9on + Achieve 5σ! Select a classifica9on method! Pre-‐processing! Choose hyper-‐params! Train! Op9mize for accuracy! SVM Decision Trees NN …..….. Integrate AMS directly in training during Gradient Boos9ng (John) during node split in random forest (John) Weighted Classifica9on Cascades Two par9cipants observe that AMS can be refactorized and its terms can be rewriHen in terms of their convex conjugate form – which allow to Fenchel-‐Young inequality from convex op9miza9on liHerature. Ref: hHp://arxiv.org/pdf/1409.2655v2.pdf, Mackey & Brian Op9miza9on of AMS becomes possible by a procedure they name Weigthed ClassificaIon Cascades.(Rank: 461th) ? ? ? ? ? Gradient boos9ng methods fit a classifier to the 'per data point loss' and since AMS is not a sum of per data point (event) losses, it's not obvious how to do use AMS as a loss in gradient boos9ng (Andre Holzner) AMS: 3.3 è The node split works by looking for the split that maximises the AMS of one side of the split when predic9ng it as pure signal (John) An alterna9ve may be to « use AUC in gradient boos9ng 9ll you get to the max cv result and then tried to move forward with an AMS loss func9on from that point » In principle, the AMS approximate func4on is derivable (hHp://9nyurl.com/ov5pedq) at a node level (s and b being the totals of other nodes, considered constant, and x, w being the probability predic9on and weight for the node to be split) and one could rewrite the part of code where the objec9ve func9on is evaluated, replacing the sums with a different calcula9on » (Giulio Casa) C space K Space Design for sta9s9cal efficiency 1st 2nd 3rd ensembles + selec9ng a cutoff threshold that op9mise (or stabilise AMS) Design strategy analysis for HiggsML challenge teams
- 30. Reduce within-‐class imbalance C K Dealing with CIP By adjus4ng class distribu4on Working in input space Re-‐represen4ng inputs Local distor4on Produce an embedding Change spa4al resolu4on For some X X is a support vector With raw data Feature engineering Exploratory (knowledge or intui4on based Automated Gene4c Algoritms (Wasilowski, Chen, 2009) Reduce between-‐class imbalance Reduce both Costs are known Oversampling signals Undersampling the background Iden4fying class distribu4on Progressive sampling by duplica4ng by synthesizing new points SMOTE, (Chawla, Bowyer et al. 2002) MSMOTE (Hu et al, 2009 ) Borderline SMOTE (Han et al, 2005) ) Adap4ve Synthe4c Sampling (He et al, 2008 ) SafeLevel Sampling (Bunkhumpornpat et al 2008 ) resample each mixture contains all signals + some background Such that all background points are used at least in one mixture Use meta-‐learning (Chan, Stolfo, 2001) Use SVM ensemble (Yan, Lin et al, 2003) Remove reduntant (Kubat, Matwia, 1997 Remove border regions with background examples (Kubat, Matwia, 1997) Reduce overlap Preferen4al sampling Remove background whose average distance to its 3 NN is smallest (Mani, Zhang, 2003) By adap4ng algorithms Improve predic4ve accuracy Reduce predic4ve variance Alterna4ve search techniques Non-‐greedy methods Gene4c Alg. Detect rare events TimeWeaver ( ) Discover small disjuncts (Carvahlo, Freitas, ) Change evalau4on metrics Simulated Annealing Depth-‐bound exhaus4ve Brute () Laplace es4mate Evaluate small disjuncts separately Quinlan, () Modify defini4on of learning Bias induc4on towards specificity Minimize error costs Change levels of learning Cascade of learners Learn only rare class () Two-‐level learnig () Unknown Costs Modify base learner Max Specificity (Acker, Porter, 1989) Specificity for small disjuncts (Ting, 1989) Base is a Tree Learner Split arributes are selected to minimise total expected cost Base is a NN Cost-‐weighted error propaga4on Relabeling for min expected cost Test data Training data Weigh4ng (Ting, 1998) CSC (Wiren, Franck, 2005) MetaCost (Domingos, 1999) Cos4ng (Zadrony et al, 2003) Preprocess ing Cost-‐based sampling Empirical Threshold Sesng Plot total cost for various thresholds Choose min using plot With Cross Valida4on by choosing less steep hills Thresholding (Sheng, Ling, 2006) Using ensembles Using cross valida4on Cost-‐ Sensi4ve Boos4ng Imbalance d IVotes () AdaCost ( ) Using sampling to alter weight distribu4on Boos4ng CSB () RareBoost ( ) MSMOTE Boost () SMOTE Boost () Data Boost-‐ IM () RUSBoost () Bagging Overbagging ( ) Underbagging () Under-‐ Over-‐ Bagging () Dicovery Problem Cross-‐Va Ensemb Gradient loss' and losses, it boos9ng AMS: 3.3 maximise as pure s An altern you get t with an A In princip (hHp://9 the total being th be split) objec9ve different 1 2 3 4 5 Data science as a new fron9er for design A. Kazakci, ICED’15 (submiHed)
- 31. Warm-up exercice • « Design »a trip for 4 person for a week for less than a thousand euros 31! Akın O. Kazakçı, MINES ParisTech!
- 32. • Not finished 32! AkınO. Kazakçı, MINES ParisTech!
- 33. DKCP process: LinearisingC-K dynamics 33! Akın O. Kazakçı, MINES ParisTech! Proven methodology: -‐ Developped at Mines ParisTech (TMCI) with RATP and Thalès Avionics -‐ 40+ KCP by researchers (2002-‐2014) -‐ 2 PhD Projects (Arnoux, 2013; Klasing Chen, in process) -‐ Now, a network of specialist consultants Ini4alisa4on [K] Knowledge sharing Workshops [P] Project building [C] IFM-‐Design Workshops [RUN]
- 34. Limits of traditionalmethods for collective creativity Consensus& Shared understanding Originality Participative Seminars Creative Commandos è Classical methods do not allow generating concepts that are both breakthrough and shared! Fixa9on Phenomena Isola9on Phenomena 34! Akın O. Kazakçı, MINES ParisTech!
- 35. DKCP : Organisingfor shared breakthrough projects Consensus& Shared understanding Originality Fixa9on Phenomena Isola9on Phenomena A method for steering breakthrough process 35! Akın O. Kazakçı, MINES ParisTech!
- 36. DKCP process: LinearisingC-K dynamics 36! Management of the cogni4ve and social aspects (KCP facilitators) Innova4on effort (Par9cipants; 20-‐50) D K C P Pré-‐C Pré-‐K Project organisa9on Defining and pre-‐explora9on of K pockets Sharing and integra9ng K Orienta9on of phase C Guided crea9vity Building ac9onnable strategies Akın O. Kazakçı, MINES ParisTech! Ini4alisa4on [K] Knowledge sharing Workshops [P] Project building [C] IFM-‐Design Workshops [RUN]
- 37. What is thedominant design of PP-ML? 37! Akın O. Kazakçı, MINES ParisTech! …how do we break it? C0: Bringing P.Physics and ML closer Why this C0? – What’s the value? Nobel prizes? Papers? Rich&Famous? Taking as a case the study of Higgs Keeping part of data for offline analysis Analysing all online Reconstruc9on by Kalman filters feature engineering invariant mass angles CAKE ? C0’:analysis by ML tradi9onal analysis What data to keep? What are the methods? Reconstruc9on: -‐ Several methods -‐ Inference or reconstruc9on Feature engineering Raw: Energe9c jets(?) Built: Reconstructed mass Invariant mass: how to calculate?
- 38. Thank you! Disclaimer: Copyrightsof images belong to their respective owners. 38! Akın O. Kazakçı, MINES ParisTech! Akın O. Kazakçı akin.kazakci@mines-paristech.fr Feel free to contact me for more: